Interpretable machine learning for materials design-Reference-Cited by-同舟云学术

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Interpretable machine learning for materials design

Published:2023-10-12 Issue:20 Volume:38 Page:4477-4496
ISSN:0884-2914
Container-title:Journal of Materials Research
language:en
Short-container-title:Journal of Materials Research

Author:

Dean James,Scheffler Matthias,Purcell Thomas A. R.,Barabash Sergey V.,Bhowmik Rahul,Bazhirov Timur^ORCID

Funder

Small Business Innovation Research

H2020 European Institute of Innovation and Technology

Deutsche Forschungsgemeinschaft

Alexander von Humboldt Postdoctoral Fellowship Program

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science

Link

https://link.springer.com/content/pdf/10.1557/s43578-023-01164-w.pdf

Reference118 articles.

1. C. Draxl, M. Scheffler, Big data-driven materials science and Its FAIR data infrastructure, in Handbook of materials modeling: methods: theory and modeling. ed. by W. Andreoni, S. Yip (Springer International Publishing, Cham, 2020), pp.49–73. https://doi.org/10.1007/978-3-319-44677-6_104

2. A..C. Mater, M..L. Coote, Deep learning in chemistry. J. Chem. Info. Model. 59(6), 2545–2559 (2019). https://doi.org/10.1021/acs.jcim.9b00266

3. K..T. Butler, D..W. Davies, H. Cartwright, O. Isayev, Aon Walsh, Machine learning for molecular and materials science. Nature 559(7715), 547–555 (2018). https://doi.org/10.1038/s41586-018-0337-2

4. L. Bornmann, R. Mutz, Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J. Assoc. Info. Sci. Technol. 66(11), 2215–2222 (2015). https://doi.org/10.1002/asi.23329

5. J. Derek, Price little science (Columbia University Press, New York, 1963). https://doi.org/10.7312/pric91844

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